The two previous works which solve the same MTL problem studied in this paper are Multi-task boosting (MT-B) [1]} and Multi-Task ExtraTrees (MT-ET) [2]}.
The main drawback of MT-B is its computational complexity since it trains \(T+1\)  forests, and despite being simple and direct, it derives low empirical performance.
In MT-ET, the authors propose to perform a random task split under some predefined probability.
The task split is conducted by sorting the tasks by their ratio of positive samples at this node and find the best split threshold with the highest information gain.
The disadvantage of MT-ET is that the decision of task split is randomly determined and is thus not reasonable since it fails to leverage any task related information, we have verified this through experiments of TSGB\(_{\lambda }\) .
